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Representation learning with a transformer by contrastive learning for money laundering detection

arXiv 2507.08835 ↗First posted 7 Jul 2025cs.LG cs.AI math.STcited by 0

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Abstract (arXiv)

The present work tackles the money laundering detection problem. A new procedure is introduced which exploits structured time series of both qualitative and quantitative data by means of a transformer neural network. The first step of this procedure aims at learning representations of time series through contrastive learning (without any labels). The second step leverages these representations to generate a money laundering scoring of all observations. A two-thresholds approach is then introduced, which ensures a controlled false-positive rate by means of the Benjamini-Hochberg (BH) procedure. Experiments confirm that the transformer is able to produce general representations that succeed in exploiting money laundering patterns with minimal supervision from domain experts. It also illustrates the higher ability of the new procedure for detecting nonfraudsters as well as fraudsters, while keeping the false positive rate under control. This greatly contrasts with rule-based procedures or the ones based on LSTM architectures.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 58 references that are in the map. First posted on arXiv 7 Jul 2025.

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23 of 58 references.

References · 23

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11 more
  1. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 207
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  7. 2020What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encodingcited by 52
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  10. 2021Anti-Money Laundering Alert Optimization Using Machine Learning with Graphscited by 7
  11. 2024Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Taskscited by 2

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